Next Generation Efficient and Sustainable AI | UDaily

The human brain is an amazing organ, as any neuroscientist can attest. And its ability to collect, store, analyze and use information is also intriguing to physicists, engineers and computer scientists.

Benjamin Jungfleischassociate professor of physics at the University of Delaware, is among them.

Jungfleisch, who joined the UD faculty in 2018, is an expert in Magnon spintronics. He uses lasers to explore the dynamics of magnetic nanostructures – tiny magnets that can be used to store and direct information through a circuit.

A primary focus of his work is finding brain-inspired ways to develop low-energy computing using interacting nanomagnets as the command center.

Neurons are the information processors of the brain, with electrical and chemical signals that carry information between neurons. In a similar way, magnons—the fundamental quantum excitations that make up the “magnetic waves” or “spin waves” in a magnetic system—perform a similar process through arrays of magnetic nanostructures, carrying and processing information in ways that could lead to faster process, more energy efficient processors and even artificial intelligence (AI) devices.

This addresses a critical need, especially now as AI power consumption skyrockets. AI has tremendous potential for our world. But its complexity requires intensive computing power and an increasing number of data centers to manage and satisfy the computing demand. Without innovative solutions, energy will be a growing problem for society, industry and the climate.

The National Science Foundation recognized the importance of Jungfleisch’s work with a 2024 CAREER award, a five-year grant worth just over $798,000, to support his research team’s efforts to develop low-power computing and processing methods using these magnetic nanostructures. The project is also supported by the Established Program for the Stimulation of Competitive Research (EPSCoR).

Jungfleisch works with nanomagnetic networks, which can be compared to the neural networks of the brain, the pathways used to move signals. Magnon connections are similar to “synapses” that transmit signals along specific circuits.

“These interacting arrays of nanomagnets are essentially just little magnets,” Jungfleisch said, “like the ones you have on your refrigerator and the ones kids play with. They have a north pole and a south pole. And if you make them very small—on the nanometer scale—you can pattern them with state-of-the-art lithography, which we have available here.”

When Jungfleisch says “tiny,” he’s talking about things you can’t see with your eyes. Nanoscale structures are measured in nanometers. It takes more than 25 million nanometers to make one inch. Much of the work is being done in UD’s Nanofabrication Facility, housed in the Patrick T. Harker Interdisciplinary Science and Engineering (ISE) Laboratory.

“You can make grids out of them and they interact,” he said. “They can store information – very similar to what neurons in our brains do. And the neurons are all connected in a network. So we place these nanomagnets in a grid and they sense each other.”

Traditional computers use a processor and memory.

“The data is constantly mixed between the two, and it’s extremely inefficient,” Jungfleisch said.

Devices using interacting nanomagnets offer multiple advantages.

“These structures can do everything,” he said. “We don’t need electrons, because we use magnetic excitations. And second, we can do processing and storage at the same time in the same drive.

“There are specific tasks, like artificial intelligence, where it can be useful – what we’re doing with ChatGPT, for example, or chatbots that have come out recently for creating images.”

These arrays of nanomagnets can be trained, Jungfleisch said. They keep a history and remember the state they are in, but they must also be susceptible to change and retraining—neuromorphic changes, they’re called.